Using Artificial Neural Networks to Model the Surface Roughness of Massive Wooden Edge-Glued Panels Made of Scotch Pine (Pinus sylvestris L.) in a Machining Process with Computer Numerical Control

Abstract In this paper, an effective process optimization approach based on artificial neural networks with a back propagation algorithm and response surface methodology including central composite design is presented for the modeling and prediction of surface roughness in the wire electrical discharge machining process. In the development of predictive models, cutting parameters of pulse duration, open circuit voltage, wire speed and dielectric flushing are considered as model variables. After experiments are carried out, the analysis of variance is implemented to identify the contribution of uncontrollable process parameters effecting surface roughness. Then, a comparative analysis of the proposed approaches is carried out to determine the most efficient one. The performance of the developed artificial neural networks and response surface methodology predictive models is tested for prediction accuracy in terms of the coefficient of determination and root mean square error metrics. The results indicate that an artificial neural networks model provides more accurate prediction than the response surface methodology model.

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Applicationof Computer Visionfor Polishing RobotinAutomotive Manufacturing Industries

EMITTER International Journal of Engineering Technology ◽

10.24003/emitter.v2i2.22 ◽

2014 ◽

Vol 2 (2) ◽

Author(s):

Adnan Rachmat Anom Besari ◽

Ruzaidi Zamri ◽

Md. Dan Md. Palil ◽

Anton Satria Prabuwono

Keyword(s):

Neural Networks ◽

Surface Roughness ◽

Artificial Neural Networks ◽

Surface Defects ◽

Vision System ◽

Real Data ◽

Surface Measurement ◽

Vision Sensor ◽

Skilled Workers ◽

Artificial Neural

Polishing is a highly skilled manufacturing process with a lot of constraints and interaction with environment. In general, the purpose of polishing is to get the uniform surface roughness distributed evenly throughout part’s surface. In order to reduce the polishing time and cope with the shortage of skilled workers, robotic polishing technology has been investigated. This paper studies about vision system to measure surface defects that have been characterized to some level of surface roughness. The surface defects data have learned using artificial neural networks to give a decision in order to move the actuator of arm robot. Force and rotation time have chosen as output parameters of artificial neural networks. Results shows that although there is a considerable change in both parameter values acquired from vision data compared to real data, it is still possible to obtain surface defects characterization using vision sensor to a certain limit of accuracy. The overall results of this research would encourage further developments in this area to achieve robust computer vision based surface measurement systems for industrial robotic, especially in polishing process.Keywords: polishing robot, vision sensor, surface defects, and artificial neural networks

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Properties of the Surface Layer After Trochoidal Milling and Brushing: Experimental Study and Artificial Neural Network Simulation

Applied Sciences ◽

10.3390/app10010075 ◽

2019 ◽

Vol 10 (1) ◽

pp. 75

Author(s):

Monika Kulisz ◽

Ireneusz Zagórski ◽

Jakub Matuszak ◽

Mariusz Kłonica

Keyword(s):

Neural Networks ◽

Surface Roughness ◽

Artificial Neural Networks ◽

Cutting Speed ◽

Mg Alloy ◽

Polar Component ◽

Maximum Height ◽

Roughness Parameters ◽

Surface Geometry ◽

Artificial Neural

The aim of this study was to investigate the effect of milling and brushing cutting data settings on the surface geometry and energy parameters of two Mg alloy substrates: AZ91D and AZ31. In milling, the cutting speed and the trochoidal step were modified (vc = 400–1200 m/min and str = 5–30%) to investigate how they affect selected 2D (Rz, Rku, Rsk, RSm, Ra) and 3D (Sa, Sz, Sku, Ssk) roughness parameters. The brushing treatment was carried out at constant parameters: n = 5000 rev/min, vf = 300 mm/min, ap = 0.5 mm. The surface roughness of specimens was assessed with the Ra, Rz, and RSm parameters. The effects of the two treatments on the workpiece surface were analyzed comparatively. It was found that the roughness properties of the machined surface may be improved by the application of a carbide milling cutter and ceramic brush. The use of different machining data was also shown to impact the surface free energy and its polar component of Mg alloy specimens. Complementary to the results from the experimental part of the study, the investigated machining processes were modelled by means of statistical artificial neural networks (the radial basis function and multi-layered perceptron). The artificial neural networks (ANNs) were shown to perform well as a tool for the prediction of Mg alloy surface roughness parameters and the maximum height of the profile (Rz) after milling and brushing.

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Surface roughness prediction of particulate composites using artificial neural networks in turning operation

Decision Science Letters ◽

10.5267/j.dsl.2015.3.001 ◽

2015 ◽

pp. 419-424 ◽

Cited By ~ 1

Author(s):

Mohammad Ramezani

Keyword(s):

Neural Networks ◽

Surface Roughness ◽

Artificial Neural Networks ◽

Particulate Composites ◽

Turning Operation ◽

Surface Roughness Prediction ◽

Roughness Prediction ◽

Artificial Neural

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Modeling & Analysis of End Milling Process Parameters Using Artificial Neural Networks

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.592-594.2733 ◽

2014 ◽

Vol 592-594 ◽

pp. 2733-2737 ◽

Cited By ~ 1

Author(s):

G. Harinath Gowd ◽

K. Divya Theja ◽

Peyyala Rayudu ◽

M. Venugopal Goud ◽

M .Subba Roa

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Process Parameters ◽

End Milling ◽

Milling Process ◽

Machining Process ◽

Depth Of Cut ◽

Machining Parameters ◽

Artificial Neural ◽

End Milling Process

For modeling and optimizing the process parameters of manufacturing problems in the present days, numerical and Artificial Neural Networks (ANN) methods are widely using. In manufacturing environments, main focus is given to the finding of Optimum machining parameters. Therefore the present research is aimed at finding the optimal process parameters for End milling process. The End milling process is a widely used machining process because it is used for the rough and finish machining of many features such as slots, pockets, peripheries and faces of components. The present work involves the estimation of optimal values of the process variables like, speed, feed and depth of cut, whereas the metal removal rate (MRR) and tool wear resistance were taken as the output .Experimental design is planned using DOE. Optimum machining parameters for End milling process were found out using ANN and compared to the experimental results. The obtained results provβed the ability of ANN method for End milling process modeling and optimization.

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